Best AI tools for< Chemist >

C&EN, a publication of the American Chemical Society, provides the latest news and insights on the chemical industry, including research, technology, business, and policy. It covers a wide range of topics, including analytical chemistry, biological chemistry, business, careers, education, energy, environment, food, materials, people, pharmaceuticals, physical chemistry, policy, research integrity, safety, and synthesis.

Jotlify is an AI-powered platform that simplifies complex research papers, making them accessible and easy to understand for students, researchers, professionals, and curious minds. It transforms dense academic content into engaging stories and insights, bridging the gap between complex research and easy understanding. With Jotlify, users can uncover stories and insights that can transform their understanding and impact various aspects of their lives.

Knowee is an AI-powered web application that provides 24/7 homework help, AI tutoring, and course notes. It offers high accuracy answers surpassing GPT-4 for various academic tasks. Users can upload images or drag and drop questions for assistance, get step-by-step solutions, and master diagrams. Knowee helps students review tough concepts, explore relevant questions, and enhance their grades across all subjects.

XtalPi is a world-leading technology company driven by artificial intelligence (AI) and robotics to innovate in the fields of life sciences and new materials. Founded in 2015 at the Massachusetts Institute of Technology (MIT), the company is committed to realizing digital and intelligent innovation in the fields of life sciences and new materials. Based on cutting-edge technologies and capabilities such as quantum physics, artificial intelligence, cloud computing, and large-scale experimental robot clusters, the company provides innovative technologies, services, and products for global industries such as biomedicine, chemicals, new energy, and new materials.

Variational AI is a company that uses generative AI to discover novel drug-like small molecules with optimized properties for defined targets. Their platform, Enki™, is the first commercially accessible foundation model for small molecules. It is designed to make generating novel molecule structures easy, with no data required. Users simply define their target product profile (TPP) and Enki does the rest. Enki is an ensemble of generative algorithms trained on decades worth of experimental data with proven results. The company was founded in September 2019 and is based in Vancouver, BC, Canada.

Iambic Therapeutics is a cutting-edge AI-driven drug discovery platform that tackles the most challenging design problems in drug discovery, addressing unmet patient need. Its physics-based AI algorithms drive a high-throughput experimental platform, converting new molecular designs to new biological insights each week. Iambic's platform optimizes target product profiles, exploring multiple profiles in parallel to ensure that molecules are designed to solve the right problems in disease biology. It also optimizes drug candidates, deeply exploring chemical space to reveal novel mechanisms of action and deliver diverse high-quality leads.

Allchemy is a resource-aware AI platform for drug discovery. It combines state-of-the-art computational synthesis with AI algorithms to predict molecular properties. Within minutes, Allchemy creates thousands of synthesizable lead candidates meeting user-defined profiles of drug-likeness, affinity towards specific proteins, toxicity, and a range of other physical-chemical measures. Allchemy encompasses the entire resource-to-drug design process and has been used in academic, corporate and classified environments worldwide to: Design synthesizable leads targeting specific proteins Evolve scaffolds similar to desired drugs Design “circular” drug syntheses from renewable materials Interface with and instruct automated synthesis platforms and optimize pilot-scale processes Operate “iterative synthesis” schemes Predict side reactions and create forensic “synthetic signatures” of hazardous/toxic molecules Design synthetic degradation and recovery cycles for various types of feedstocks and functional target molecules

Lavo Life Sciences is an AI-accelerated crystal structure prediction application that helps in drug development by providing accurate predictions for small molecule drugs. The application utilizes AI technology to optimize solid-state formulations, reduce turnaround time, mitigate risks, and discover novel polymorphs, ultimately streamlining the pharmaceutical research and development process.

Recursion is a techbio company that uses artificial intelligence to accelerate drug discovery. The company's platform combines hardware, software, and data to create a more efficient and effective drug discovery process. Recursion has a broad pipeline of drug candidates in development, and it has partnered with several leading pharmaceutical companies. The company is headquartered in Salt Lake City, Utah.

The Institute for Protein Design is a research institute at the University of Washington that uses computational design to create new proteins that solve modern challenges in medicine, technology, and sustainability. The institute's research focuses on developing new protein therapeutics, vaccines, drug delivery systems, biological devices, self-assembling nanomaterials, and bioactive peptides. The institute also has a strong commitment to responsible AI development and has developed a set of principles to guide its use of AI in research.

Valo is a company that uses AI-driven technology to transform the discovery and development of life-changing medicines. They combine machine learning, tissue biology, and patient data to create a suite of powerful capabilities that bring the future of drug discovery and development to bear. Valo's team of software engineers, data scientists, biologists, medicinal chemists, and big-picture thinkers are dedicated to advancing the combined power of technology and patient data.

Cradle is a protein engineering platform that uses machine learning to design improved protein sequences. It allows users to import assay data, generate new sequences, test them in the lab, and import the results to improve the model. Cradle can be used to optimize multiple properties of a protein simultaneously, and it has been used by leading biotech teams to accelerate new and ongoing projects.

Atomwise is an artificial intelligence (AI)-driven drug discovery company that uses machine learning to discover and develop new small molecule medicines. The company's AI engine combines the power of convolutional neural networks with massive chemical libraries to identify new drug candidates. Atomwise has a wholly owned pipeline of drug discovery programs and also partners with other pharmaceutical companies to co-develop drugs. The company's investors include prominent venture capital firms and pharmaceutical companies.

Atomwise is an AI-powered drug discovery company that uses machine learning to identify new small molecule medicines. The company's platform combines the power of convolutional neural networks with massive chemical libraries to discover new drug candidates. Atomwise has a portfolio of wholly owned and co-developed pipeline assets, and is backed by prominent investors.

LuxAuraAI is an AI-powered platform that helps users discover their perfect skincare routine through a personalized quiz. By leveraging expert knowledge and science-backed results, LuxAuraAI provides tailored skincare recommendations based on individual skin needs. The platform aims to simplify the process of finding suitable skincare products by incorporating insights from dermatologists and skincare experts. LuxAuraAI is designed to offer a convenient and effective solution for those seeking a skincare regimen that is both personalized and backed by scientific research.

Chemprop is a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. Originally developed for research purposes, Chemprop offers a comprehensive set of tools and features for training models and analyzing molecular representations. The package underwent a recent major release (v2.0.0) with significant improvements and updates.

EasySBC is a web-based application that provides solutions for Squad Building Challenges (SBCs) in the popular video game FIFA 23. It features an AI-powered squad builder that helps users create optimal squads for SBCs, taking into account player ratings, chemistry, and other factors. The application also includes a comprehensive database of players and their attributes, as well as meta ratings that indicate the effectiveness of players in different positions and formations.

One Drop has developed a next-generation intradermal continuous glucose monitoring (CGM) device. Advanced material science, chemistry, and electronics make the One Health CGM among the most innovative body-worn sensors—explicitly designed to meet the needs of people with type 2 diabetes. By integrating proprietary micro-needle technology and AI-enabled precision guidance, the minimally invasive One Health CGM will deliver pain-free, needle-free wear and unprecedented access to a population currently underserved by CGM.

Kuano is an AI tool that focuses on redefining drug discovery using Quantum and AI technologies. The platform offers world-class scientific expertise in quantum physics, AI, and medicinal chemistry to revolutionize the drug design process. Kuano aims to leverage cutting-edge technologies to accelerate the discovery of new drugs and improve healthcare outcomes.

Gauth is an AI-powered homework helper that provides step-by-step solutions to STEM problems. It utilizes advanced algorithms and AI technology to solve complex math, statistics, calculus, physics, chemistry, biology, and history questions. Gauth also offers live expert support, with thousands of real experts available 24/7 to provide detailed explanations and guidance. The app is designed to help students of all grades and levels conquer challenging homework problems and improve their understanding of STEM subjects.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It allows users to run benchmarking tasks on models with existing presets, offering predefined parameters and processing steps. The library facilitates benchmarking models on the entire suite, addressing challenges such as prompt structure, parsing, and scoring methods. Users can contribute to the project by following the developer notes.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It enables benchmarking across a wide range of API-based models and employs an LLM-based extractor as a fallback mechanism. Users can evaluate models on specific chemistry topics and run comprehensive evaluations across all topics in the benchmark suite. The tool facilitates seamless benchmarking for any model supported by LiteLLM and allows running non-API hosted models.

A toolkit for computational chemistry research, featuring tools to facilitate automated workflows. Includes tools for NMR prediction, dynamic catalysis research, proton transfer analysis, amorphous oxides structure analysis, reweighting, and more. Users can install 'ai2-kit' via pip and explore various domain-specific and general tools for processing system data and filtering structures by model deviation.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

The 'LLMs-in-science' repository is a collaborative environment for organizing papers related to large language models (LLMs) and autonomous agents in the field of chemistry. The goal is to discuss trend topics, challenges, and the potential for supporting scientific discovery in the context of artificial intelligence. The repository aims to maintain a systematic structure of the field and welcomes contributions from the community to keep the content up-to-date and relevant.

This repository serves as an educational resource for pharmaceutical and chemistry students to learn the basics of Deep Learning through a collection of Jupyter Notebooks. The content covers various topics such as Introduction to Jupyter, Python, Cheminformatics & RDKit, Linear Regression, Data Science, Linear Algebra, Neural Networks, PyTorch, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks, Autoencoders, Graph Neural Networks, and Summary. The notebooks aim to provide theoretical concepts to understand neural networks through code completion, but instructors are encouraged to supplement with their own lectures. The work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

LangSim is a tool developed to address the challenge of using simulation tools in computational chemistry and materials science, which typically require cryptic input files or programming experience. The tool provides a Large Language Model (LLM) extension with agents to couple the LLM to scientific simulation codes and calculate physical properties from a natural language interface. It aims to simplify the process of interacting with simulation tools by enabling users to query the large language model directly from a Python environment or a web-based interface.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

PaddleScience is a scientific computing suite developed based on the deep learning framework PaddlePaddle. It utilizes the learning ability of deep neural networks and the automatic (higher-order) differentiation mechanism of PaddlePaddle to solve problems in physics, chemistry, meteorology, and other fields. It supports three solving methods: physics mechanism-driven, data-driven, and mathematical fusion, and provides basic APIs and detailed documentation for users to use and further develop.

Docling simplifies document processing, parsing diverse formats including advanced PDF understanding, and providing seamless integrations with the general AI ecosystem. It offers features such as parsing multiple document formats, advanced PDF understanding, unified DoclingDocument representation format, various export formats, local execution capabilities, plug-and-play integrations with agentic AI tools, extensive OCR support, and a simple CLI. Coming soon features include metadata extraction, visual language models, chart understanding, and complex chemistry understanding. Docling is installed via pip and works on macOS, Linux, and Windows environments. It provides detailed documentation, examples, integrations with popular frameworks, and support through the discussion section. The codebase is under the MIT license and has been developed by IBM.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

MMLU-Pro is an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. It integrates more challenging, reasoning-focused questions and increases answer choices per question, significantly raising difficulty. The dataset comprises over 12,000 questions from academic exams and textbooks across 14 diverse domains. Experimental results show a significant drop in accuracy compared to the original MMLU, with greater stability under varying prompts. Models utilizing Chain of Thought reasoning achieved better performance on MMLU-Pro.

SciCode is a challenging benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 subdomains from 6 domains. The benchmark offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. SciCode demonstrates a realistic workflow of identifying critical science concepts and facts and transforming them into computation and simulation code, aiming to help showcase LLMs' progress towards assisting scientists and contribute to the future building and evaluation of scientific AI.

Language Models is a Python package that provides building blocks to explore large language models with as little as 512MB of RAM. It simplifies the usage of large language models from Python, ensuring all inference is performed locally to keep data private. The package includes features such as text completions, chat capabilities, code completions, external text retrieval, semantic search, and more. It outperforms Hugging Face transformers for CPU inference and offers sensible default models with varying parameters based on memory constraints. The package is suitable for learners and educators exploring the intersection of large language models with modern software development.

Docling is a tool that bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. It can convert any PDF document to JSON or Markdown format, understand detailed page layout, reading order, recover table structures, extract metadata such as title, authors, references, and language, and optionally apply OCR for scanned PDFs. The tool is designed to be stable, lightning fast, and suitable for macOS and Linux environments.

Everything about water-fuelled car

Expert in nano silver tech, includes contact info in ads.

Identifies signals in 1H and 13C NMR spectra, suggesting possible structures.

Turbocharge your research and streamline your path to breakthrough findings. Leveraging the vast resources of PubChem, this GPT taps into a wealth of chemical data—from substances to proteins and patents—unleashing the full potential of your data for richer, more informed discoveries.

Analyzes IR spectra, prompts for uploads, and details findings in tables.

Expert in automation engineering, generating Python code for Opentrons SDK.

Advanced AI for chemistry, offering innovative solutions, process optimizations, and safety assessments, powered by OpenAI.

Professional chemistry assistant, SMILES/SMART supported molecule and reaction diagrams, and more!

Expert in generating and explaining mathematical, chemical, and computational formulas.

Professional terpene educator using analogies.

Expert in salt-related topics, from cooking uses to types.

A scientific guide to chemicals and their properties.

An educational guide to exploring the world of chemistry.

Explore, render, and ask questions about materials discovered in the GNoME project

Expert on lye, providing detailed chemical information and safety guidelines.

Explore the fascinating world of elements with in-depth insights on chemistry, periodic table, and elemental properties. Dive into our comprehensive guide for a deeper understanding of the elements' significance in science and everyday life.

Questo programma fornisce spiegazioni chiare su un'ampia gamma di argomenti di chimica. Gli utenti possono imparare tutto, dai concetti di base della chimica alle teorie più complesse. È stato progettato per rendere la chimica facile da capire per tutti.

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